Performing Data Analysis Using IBM Spss

Features easy-to-follow insight and clear guidelines to perform data analysis using IBM SPSS®

Performing Data Analysis Using IBM SPSS® uniquely addresses the presented statistical procedures with an example problem, detailed analysis, and the related data sets. Data entry procedures, variable naming, and step-by-step instructions for all analyses are provided in addition to IBM SPSS point-and-click methods, including details on how to view and manipulate output.

Designed as a user’s guide for students and other interested readers to perform statistical data analysis with IBM SPSS, this book addresses the needs, level of sophistication, and interest in introductory statistical methodology on the part of readers in social and behavioral science, business, health-related, and education programs. Each chapter of Performing Data Analysis Using IBM SPSS covers a particular statistical procedure and offers the following: an example problem or analysis goal, together with a data set; IBM SPSS analysis with step-by-step analysis setup and accompanying screen shots; and IBM SPSS output with screen shots and narrative on how to read or interpret the results of the analysis.

The book provides in-depth chapter coverage of:

• IBM SPSS statistical output
• Descriptive statistics procedures
• Score distribution assumption evaluations
• Bivariate correlation
• Regressing (predicting) quantitative and categorical variables
• Survival analysis
• t Test
• ANOVA and ANCOVA
• Multivariate group differences
• Multidimensional scaling
• Cluster analysis
• Nonparametric procedures for frequency data

Performing Data Analysis Using IBM SPSS is an excellent text for upper-undergraduate and graduate-level students in courses on social, behavioral, and health sciences as well as secondary education, research design, and statistics. Also an excellent reference, the book is ideal for professionals and researchers in the social, behavioral, and health sciences; applied statisticians; and practitioners working in industry.

LAWRENCE S. MEYERS, PhD, is Professor in the Depart-ment of Psychology at California State University, Sacramento. The author of numerous books, Dr. Meyers is a member of the Association for Psychological Science and the Society for Industrial and Organiza-tional Psychology.

GLENN C. GAMST, PhD, is Chair and Professor in the Department of Psychology at the University of La Verne. His research interests include univariate and multivariate statistics as well as multicultural community mental health outcome research.

A. J. Guarino, PhD, is Professor of Biostatistics at Massachusetts General Hospital, Institute of Health Professions, where he serves as the methodologist for capstones and dissertations as well as teaching advanced Biostatistics courses. Dr. Guarino is also the statistician on numerous National Institutes of Health grants and coauthor of several statistical textbooks.

Part 1 Getting Started With IBM SPSS

1 Introduction to IBM SPSS

2 Entering Data in IBM SPSS

3 Importing Data From Excel to IBM SPSS

Part 2 IBM SPSS Statistical Output

4 Performing Statistical Procedures

5 Editing Output

6 Saving and Copying Output

Part 3 Manipulating Data

7 Sorting and Selecting Cases

8 Splitting Data Files

9 Merging Cases and Variables

Part 4 Descriptive Statistics Procedures

10 Frequencies

11 Descriptives

12 Explore

Part 5 Simple Data Transformations

13 Standardizing Data with z Scores

14 Recoding Variables

15 Visual Binning

16 Computing New Variables

17 Transforming Dates to Time

Part 6 Evaluating Score Distribution Assumptions

18 Detecting Univariate Outliers

19 Detecting Multivariate Outliers

20 Assessing Distribution Shape: Normality, Skewness, and Kurtosis

21 Transforming Data to Remedy Statistical Assumption Violations

Part 7 Bivariate Correlation

22 Bivariate Correlation: Pearson r

23 Spearman Rho and Kendall Tau-b

Part 8 Regressing (Predicting) Quantitative Variables

24 Simple Linear Regression

25 Centering the Predictor in Simple Linear Regression

26 Multiple Linear Regression

27 Hierarchical Linear Regression

28 Polynomial (Curve Estimation) Regression

29 Multilevel Modeling

Part 9 Regressing (Predicting) Categorical Variables

30 Binary Logistic Regression

31 ROC Analysis

32 Multinomial Logistic Regression

Part 10 Survival Analysis

33 Life Tables

34 Kaplan-Meier

35 Cox Regression

Part 11 Reliability

36 Reliability Analysis: Internal Consistency

37 Inter-Rater Reliability

Part 12 Analyses of Structure

38 Principal Components and Factor Analysis

39 CFA

Part 13 Evaluating Models

40 Simple Mediation

41 Path Analysis Using Multiple Regression

42 Path Analysis Using Amos

43 SEM

Part 14 t Test

44 Single Sample Test

45 Independent Groups t Test

46 Correlated Samples t Test

Part 15 ANOVA and ANCOVA

47 One-Way Between Subjects ANOVA in GLM

48 Trend Analysis (Polynomial Contrasts)

49 One-Way Between Subjects ANCOVA

50 Two-Way Between Subjects ANOVA

51 One-Way Within Subjects ANOVA

52 One-Way Repeated Linear Mixed Models

53 Two-Way Mixed Design

Part 16 Multivariate Group Differences

54 One-Way Between Subjects MANOVA

55 Discriminant Function Analysis

56 Two-Way Between Subjects MANOVA

Part 17 Multidimensional Scaling

57 Multidimensional Scaling: Classical Metric

58 Multidimensional Scaling: Individual Differences Scaling

Part 18 Cluster Analysis

59 Hierarchical Cluster Analysis

60 K-Means Cluster Analysis

Part 19 Nonparametric Procedures for Frequency Data

61 Binomial Test

62 One-Way Chi-Square

63 Two-Way Chi-Square: Observed Versus Expected Frequencies

64 Risk Analysis

65 Chi Square Layers

66 Hierarchical Log-linear Analysis

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